DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing

• URL: http://arxiv.org/abs/2303.09827v1
• Date: Fri, 17 Mar 2023 08:12:36 GMT
• Title: DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing
• Authors: Jihyun Lee, Seungyeon Seo, Yunsu Kim, Gary Geunbae Lee
• Abstract summary: DSTC11-Track2 aims to provide a benchmark for zero-shot, cross-domain, intent-set induction. We leveraged a multi-domain dialogue dataset to fine-tune the language model and proposed extracting Verb-Object pairs. Our approach achieved 3rd place in the precision score and showed superior accuracy and normalized mutual information (NMI) score than the baseline model.
• Score: 14.709084509818474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11). DSTC11-Track2 aims to provide a benchmark for zero-shot, cross-domain, intent-set induction. In the absence of in-domain training dataset, robust utterance representation that can be used across domains is necessary to induce users' intentions. To achieve this, we leveraged a multi-domain dialogue dataset to fine-tune the language model and proposed extracting Verb-Object pairs to remove the artifacts of unnecessary information. Furthermore, we devised the method that generates each cluster's name for the explainability of clustered results. Our approach achieved 3rd place in the precision score and showed superior accuracy and normalized mutual information (NMI) score than the baseline model on various domain datasets.

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